• Lead the design, deployment, and operation of production machine learning systems for both batch and online use cases, with a deep focus on reliability, scalability, and maintainability. • Build and improve the infrastructure for the ML lifecycle. This includes training pipelines and inference workflows. It also covers model deployment patterns, monitoring, alerting, and automating retraining. • Partner with data scientists, engineers, product managers, and domain stakeholders to translate ambiguous business problems into practical ML solutions with clear validation plans and measurable impact. • Guide the shift from prototype to a robust production system. This includes several tasks. These tasks include model packaging and orchestration. They also involve observability, documentation, and operational guardrails. • Improve developer experience for ML at Included Health by creating reusable patterns, templates, tooling, and documentation that make it easier for other engineers to ship production-grade models. • Design and optimize workflows for model evaluation, monitoring, and performance tuning, including system metrics, business metrics, and model-quality signals. • Build systems that support explainability, auditability, and safe downstream consumption of ML outputs in product and operational workflows. • Work collaboratively with the machine learning, data engineering, and application engineering teams. Define clear interfaces. These should connect data platforms, model pipelines, and product integrations. • Make pragmatic technical tradeoffs across latency, cost, complexity, and model quality, especially in real-world systems with imperfect data and evolving business requirements. • Provide technical leadership and mentorship to other engineers, raising the bar for engineering quality, operational excellence, and product-minded ML development.

• Analyze detection data including customer reports to determine which aspects of the machine learning models can be improved • Perform tasks to enable better-management of false positive detections • Analyze binary files to determine their legitimacy • Review current product detections to ensure they are performing to the company standard • Address internal questions and concerns regarding customer threat detections

Role Description Senior Machine Learning Engineer – Recommendation Systems Remote – United States Salary: $160,000 – $250,000 + Equity Please note: Candidates must be fluent in both Mandarin and English. Revive Recruitment is partnering with a well-funded, high-growth AI startup that is transforming the way millions of users discover and engage with digital content. This is an opportunity to join an ambitious engineering team building cutting-edge machine learning systems that directly influence user engagement and product growth. This is a senior individual contributor role where you'll take ownership of recommendation and search systems, working at the intersection of machine learning, large-scale data and consumer products. - Design, build and optimise large-scale recommendation and search systems for consumer applications. - Develop retrieval and ranking models, including embedding-based retrieval, candidate generation and two-tower architectures. - Build production-grade machine learning pipelines from experimentation through to deployment. - Analyse user behaviour and content signals to improve recommendations, engagement and retention. - Run A/B tests and use data-driven insights to continuously improve model performance. - Partner closely with Product, Data Science and Engineering teams to shape the future direction of the platform. - Help define the long-term machine learning architecture and best practices across the business. Qualifications - 5+ years' experience building production recommendation, ranking or search systems for consumer-facing products. - Strong Python development skills. - Hands-on experience with recommendation algorithms, ranking models and retrieval systems. - Experience with embedding models, two-tower architectures or similar recommendation frameworks. - Strong knowledge of TensorFlow or PyTorch. - Experience building scalable ML pipelines and deploying models into production. - A solid understanding of experimentation methodologies, A/B testing and performance optimisation. - Excellent communication skills with the ability to collaborate across technical and non-technical teams. - Fluency in both Mandarin and English is essential. Benefits - Competitive salary of $160,000–$250,000 plus meaningful equity. - Fully remote position within the United States. - Comprehensive healthcare and benefits package. - Opportunity to work on large-scale AI and machine learning challenges that impact millions of users. - Join a fast-growing, venture-backed startup with genuine opportunities for progression and ownership. - Work alongside a highly talented engineering team solving complex, real-world machine learning problems using the latest technologies.

Role Description The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgments and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. - Build scalable systems for training and fine-tuning large-scale models to evaluate interesting driving behaviors. - Work at the intersection of data engineering, model development, and simulation. - Provide guidance on architectural decisions and technical directions. - Own large, complex systems, driving architectures that meet technical and business objectives. - Contribute to the production and optimization of machine learning models aiming to assess Waymo’s expansive fleet of vehicles that cumulatively travel millions of miles. - Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation, model training, and evaluation. - Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. - Translate product/business goals into measurable technical deliverables, ensuring system component alignment. Qualifications - M.S. or Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience. - 5+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model. - A history of contributions to machine learning tooling and frameworks e.g. PyTorch, Jax, Tensorflow, Ray, or similar. - Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks. Requirements - 7+ years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model. - Experience in the autonomous vehicles domain, robotics, or complex simulation environments. - Familiarity with large-scale simulation platforms and their integration with ML training workflows. Benefits - Waymo employees are eligible to participate in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is $213,000 — $263,000 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level.